{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.1 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "4ce0e62306dd6a5716965d4519ada776f947e6dfc145b604b11307c10277ef29"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "来源于官方资料：[十分钟入门pandas](https://www.pypandas.cn/docs/getting_started/10min.html#%E7%94%9F%E6%88%90%E5%AF%B9%E8%B1%A1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 1. 创建数据集 "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0    1.0\n1    2.0\n2    3.0\n3    NaN\n4    4.0\ndtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 用值列表生成Serises，默认自动生成整数索引\n",
    "s = pd.Series([1, 2, 3, np.nan, 4])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "DatetimeIndex(['2021-02-20', '2021-02-21', '2021-02-22', '2021-02-23',\n               '2021-02-24', '2021-02-25'],\n              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range('20210220', periods=6)\n",
    "print(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用上述的时间索引生成数据集\n",
    "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-ac030141e94c>, line 3)",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-5-ac030141e94c>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    ...:                     'B': pd.Timestamp('20130102'),\u001b[0m\n\u001b[1;37m                                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# 使用Series字典对象生成数据集\n",
    "df2 = pd.DataFrame({'A': 1.,\n",
    "   ...:                     'B': pd.Timestamp('20130102'),\n",
    "   ...:                     'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "   ...:                     'D': np.array([3] * 4, dtype='int32'),\n",
    "   ...:                     'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "   ...:                     'F': 'foo'})\n",
    "   ...:\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# 查看不同属性列的类型\n",
    "df2.dtypes"
   ]
  },
  {
   "source": [
    "## 2. 查看对象"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-20 -0.575321  0.641131  0.277875  0.167075\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809 -0.026509\n",
       "2021-02-23 -1.421828 -0.474671  0.953416 -0.907602\n",
       "2021-02-24 -0.640503 -0.712421 -1.224029  1.860798"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>0.167075</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>-0.026509</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>-0.907602</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>-0.640503</td>\n      <td>-0.712421</td>\n      <td>-1.224029</td>\n      <td>1.860798</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809 -0.026509\n",
       "2021-02-23 -1.421828 -0.474671  0.953416 -0.907602\n",
       "2021-02-24 -0.640503 -0.712421 -1.224029  1.860798\n",
       "2021-02-25  0.815809  0.702502 -1.608049  1.129848"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>-0.026509</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>-0.907602</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>-0.640503</td>\n      <td>-0.712421</td>\n      <td>-1.224029</td>\n      <td>1.860798</td>\n    </tr>\n    <tr>\n      <th>2021-02-25</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>-1.608049</td>\n      <td>1.129848</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-02-20', '2021-02-21', '2021-02-22', '2021-02-23',\n",
       "               '2021-02-24', '2021-02-25'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
       "       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],\n",
       "       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
       "       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# 查找支持numpy的对象\n",
    "df.to_numpy()\n",
    "df2.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.542674  0.071830 -0.691878  0.319249\n",
       "std    0.791857  0.621654  1.436321  1.007462\n",
       "min   -1.421828 -0.712421 -2.834809 -0.907602\n",
       "25%   -1.051851 -0.416592 -1.512044 -0.237713\n",
       "50%   -0.607912  0.137219 -0.473077  0.070283\n",
       "75%   -0.327754  0.610046  0.282712  0.889155\n",
       "max    0.815809  0.702502  0.953416  1.860798"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>6.000000</td>\n      <td>6.000000</td>\n      <td>6.000000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-0.542674</td>\n      <td>0.071830</td>\n      <td>-0.691878</td>\n      <td>0.319249</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.791857</td>\n      <td>0.621654</td>\n      <td>1.436321</td>\n      <td>1.007462</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-1.421828</td>\n      <td>-0.712421</td>\n      <td>-2.834809</td>\n      <td>-0.907602</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-1.051851</td>\n      <td>-0.416592</td>\n      <td>-1.512044</td>\n      <td>-0.237713</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-0.607912</td>\n      <td>0.137219</td>\n      <td>-0.473077</td>\n      <td>0.070283</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-0.327754</td>\n      <td>0.610046</td>\n      <td>0.282712</td>\n      <td>0.889155</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>0.953416</td>\n      <td>1.860798</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   D         C         B         A\n",
       "2021-02-20  0.167075  0.277875  0.641131 -0.575321\n",
       "2021-02-21 -0.308114  0.284324  0.516794 -1.188967\n",
       "2021-02-22 -0.026509 -2.834809 -0.242355 -0.245231\n",
       "2021-02-23 -0.907602  0.953416 -0.474671 -1.421828\n",
       "2021-02-24  1.860798 -1.224029 -0.712421 -0.640503\n",
       "2021-02-25  1.129848 -1.608049  0.702502  0.815809"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>D</th>\n      <th>C</th>\n      <th>B</th>\n      <th>A</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>0.167075</td>\n      <td>0.277875</td>\n      <td>0.641131</td>\n      <td>-0.575321</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-0.308114</td>\n      <td>0.284324</td>\n      <td>0.516794</td>\n      <td>-1.188967</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.026509</td>\n      <td>-2.834809</td>\n      <td>-0.242355</td>\n      <td>-0.245231</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-0.907602</td>\n      <td>0.953416</td>\n      <td>-0.474671</td>\n      <td>-1.421828</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>1.860798</td>\n      <td>-1.224029</td>\n      <td>-0.712421</td>\n      <td>-0.640503</td>\n    </tr>\n    <tr>\n      <th>2021-02-25</th>\n      <td>1.129848</td>\n      <td>-1.608049</td>\n      <td>0.702502</td>\n      <td>0.815809</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# 按照轴进行排列\n",
    "df.sort_index(axis=1,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-25  0.815809  0.702502 -1.608049  1.129848\n",
       "2021-02-20 -0.575321  0.641131  0.277875  0.167075\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809 -0.026509\n",
       "2021-02-23 -1.421828 -0.474671  0.953416 -0.907602\n",
       "2021-02-24 -0.640503 -0.712421 -1.224029  1.860798"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-25</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>-1.608049</td>\n      <td>1.129848</td>\n    </tr>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>0.167075</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>-0.026509</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>-0.907602</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>-0.640503</td>\n      <td>-0.712421</td>\n      <td>-1.224029</td>\n      <td>1.860798</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# 按照值进行排列\n",
    "df.sort_values(by='B', ascending=False)"
   ]
  },
  {
   "source": [
    "## 3. 选择 "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2021-02-20   -0.575321\n",
       "2021-02-21   -1.188967\n",
       "2021-02-22   -0.245231\n",
       "2021-02-23   -1.421828\n",
       "2021-02-24   -0.640503\n",
       "2021-02-25    0.815809\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "# 选择单列数据\n",
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-20 -0.575321  0.641131  0.277875  0.167075\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809 -0.026509\n",
       "2021-02-23 -1.421828 -0.474671  0.953416 -0.907602"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>0.167075</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>-0.026509</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>-0.907602</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "# 使用[]进行切片\n",
    "df[0:3]\n",
    "df['20210220':'20210223']"
   ]
  },
  {
   "source": [
    "### 3.2 按标签访问"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "A   -0.575321\n",
       "B    0.641131\n",
       "C    0.277875\n",
       "D    0.167075\n",
       "Name: 2021-02-20 00:00:00, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "# 使用标签返回一行数据\n",
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B\n",
       "2021-02-20 -0.575321  0.641131\n",
       "2021-02-21 -1.188967  0.516794\n",
       "2021-02-22 -0.245231 -0.242355\n",
       "2021-02-23 -1.421828 -0.474671"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "# 用标签选择多列多行数据\n",
    "df.loc['20210220':'20210223', ['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "-0.5753212092336925"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "# 提取标量值\n",
    "df.loc[dates[0], 'A']"
   ]
  },
  {
   "source": [
    "### 3.3 按照位置选择\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   B         C\n",
       "2021-02-23 -0.474671  0.953416\n",
       "2021-02-24 -0.712421 -1.224029"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>-0.712421</td>\n      <td>-1.224029</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "# 类似于numpy切片\n",
    "df.iloc[3:5, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.5167938669172373"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "# 访问标量\n",
    "df.iloc[1,1]"
   ]
  },
  {
   "source": [
    "### 3.4 bool索引"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-20 -0.575321  0.641131  0.277875  0.167075\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114\n",
       "2021-02-25  0.815809  0.702502 -1.608049  1.129848"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>0.167075</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n    </tr>\n    <tr>\n      <th>2021-02-25</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>-1.608049</td>\n      <td>1.129848</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "df[df.B > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-20       NaN  0.641131  0.277875  0.167075\n",
       "2021-02-21       NaN  0.516794  0.284324       NaN\n",
       "2021-02-22       NaN       NaN       NaN       NaN\n",
       "2021-02-23       NaN       NaN  0.953416       NaN\n",
       "2021-02-24       NaN       NaN       NaN  1.860798\n",
       "2021-02-25  0.815809  0.702502       NaN  1.129848"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>NaN</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>0.167075</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>NaN</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.953416</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.860798</td>\n    </tr>\n    <tr>\n      <th>2021-02-25</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>NaN</td>\n      <td>1.129848</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "# 选择整个数据集中满足条件的数据\n",
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114    two\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809 -0.026509  three"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n      <td>two</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>-0.026509</td>\n      <td>three</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "# 使用isin进行筛选\n",
    "df3 = df.copy()\n",
    "df3['E'] = ['one', 'two', 'three', 'four', 'five', 'six']\n",
    "df3[df3['E'].isin(['two', 'three'])]"
   ]
  },
  {
   "source": [
    "### 3.5 赋值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C         D  F\n",
       "2021-02-20 -0.575321  0.641131  0.277875  0.167075  1\n",
       "2021-02-21 -1.188967  0.516794  0.284324 -0.308114  2\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809 -0.026509  3\n",
       "2021-02-23 -1.421828 -0.474671  0.953416 -0.907602  4\n",
       "2021-02-24 -0.640503 -0.712421 -1.224029  1.860798  5\n",
       "2021-02-25  0.815809  0.702502 -1.608049  1.129848  6"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>F</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>0.167075</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>-0.308114</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>-0.026509</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>-0.907602</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>-0.640503</td>\n      <td>-0.712421</td>\n      <td>-1.224029</td>\n      <td>1.860798</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2021-02-25</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>-1.608049</td>\n      <td>1.129848</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "# 用索引自动对齐\n",
    "s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20210220', periods=6))\n",
    "df['F'] = s1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C  D  F\n",
       "2021-02-20 -0.575321  0.641131  0.277875  5  1\n",
       "2021-02-21 -1.188967  0.516794  0.284324  5  2\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809  5  3\n",
       "2021-02-23 -1.421828 -0.474671  0.953416  5  4\n",
       "2021-02-24 -0.640503 -0.712421 -1.224029  5  5\n",
       "2021-02-25  0.815809  0.702502 -1.608049  5  6"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>F</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>5</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>5</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2021-02-24</th>\n      <td>-0.640503</td>\n      <td>-0.712421</td>\n      <td>-1.224029</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2021-02-25</th>\n      <td>0.815809</td>\n      <td>0.702502</td>\n      <td>-1.608049</td>\n      <td>5</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "df.loc[:, 'D'] = np.array([5]*len(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "source": [
    "## 4. 缺失值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C  D  F    E\n",
       "2021-02-20 -0.575321  0.641131  0.277875  5  1  1.0\n",
       "2021-02-21 -1.188967  0.516794  0.284324  5  2  1.0\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809  5  3  NaN\n",
       "2021-02-23 -1.421828 -0.474671  0.953416  5  4  NaN"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>F</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>5</td>\n      <td>2</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>5</td>\n      <td>3</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>5</td>\n      <td>4</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n",
    "df1.loc[dates[0]:dates[1],'E'] = 1\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C  D  F    E\n",
       "2021-02-20 -0.575321  0.641131  0.277875  5  1  1.0\n",
       "2021-02-21 -1.188967  0.516794  0.284324  5  2  1.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>F</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>5</td>\n      <td>2</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "# 删除缺失数据行\n",
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   A         B         C  D  F    E\n",
       "2021-02-20 -0.575321  0.641131  0.277875  5  1  1.0\n",
       "2021-02-21 -1.188967  0.516794  0.284324  5  2  1.0\n",
       "2021-02-22 -0.245231 -0.242355 -2.834809  5  3  5.0\n",
       "2021-02-23 -1.421828 -0.474671  0.953416  5  4  5.0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>F</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-02-20</th>\n      <td>-0.575321</td>\n      <td>0.641131</td>\n      <td>0.277875</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2021-02-21</th>\n      <td>-1.188967</td>\n      <td>0.516794</td>\n      <td>0.284324</td>\n      <td>5</td>\n      <td>2</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2021-02-22</th>\n      <td>-0.245231</td>\n      <td>-0.242355</td>\n      <td>-2.834809</td>\n      <td>5</td>\n      <td>3</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>2021-02-23</th>\n      <td>-1.421828</td>\n      <td>-0.474671</td>\n      <td>0.953416</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "# 填充\n",
    "df1.fillna(value=5)"
   ]
  },
  {
   "source": [
    "## 5. 运算"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 5.1 统计"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.07182990458251581"
      ]
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "df['B'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2021-02-20    1.268737\n",
       "2021-02-21    1.322430\n",
       "2021-02-22    0.935521\n",
       "2021-02-23    1.611383\n",
       "2021-02-24    1.484609\n",
       "2021-02-25    2.182052\n",
       "Freq: D, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "# 在行上求平均值\n",
    "df.mean(1)"
   ]
  },
  {
   "source": [
    "## 6. 合并\n",
    "### 6.1 结合"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.547341 -2.082929 -0.190950 -0.327501\n",
       "1 -1.336805  1.148632 -0.703326 -1.178525\n",
       "2 -1.114126 -1.051393  2.150010 -1.909319\n",
       "3 -0.838999 -0.167628 -0.095623 -1.230762\n",
       "4  0.300337 -0.803076 -1.305823  0.565151\n",
       "5 -0.160864  2.044033 -0.973718 -1.314739\n",
       "6  1.039741  0.924924 -0.246127 -1.589007\n",
       "7 -0.702157 -0.888025 -0.041296  0.184977\n",
       "8  0.058471 -0.833152 -1.551953  0.679490\n",
       "9 -0.521986  0.819052 -2.624007  0.340274"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.547341</td>\n      <td>-2.082929</td>\n      <td>-0.190950</td>\n      <td>-0.327501</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.336805</td>\n      <td>1.148632</td>\n      <td>-0.703326</td>\n      <td>-1.178525</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.114126</td>\n      <td>-1.051393</td>\n      <td>2.150010</td>\n      <td>-1.909319</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.838999</td>\n      <td>-0.167628</td>\n      <td>-0.095623</td>\n      <td>-1.230762</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.300337</td>\n      <td>-0.803076</td>\n      <td>-1.305823</td>\n      <td>0.565151</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-0.160864</td>\n      <td>2.044033</td>\n      <td>-0.973718</td>\n      <td>-1.314739</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1.039741</td>\n      <td>0.924924</td>\n      <td>-0.246127</td>\n      <td>-1.589007</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-0.702157</td>\n      <td>-0.888025</td>\n      <td>-0.041296</td>\n      <td>0.184977</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.058471</td>\n      <td>-0.833152</td>\n      <td>-1.551953</td>\n      <td>0.679490</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-0.521986</td>\n      <td>0.819052</td>\n      <td>-2.624007</td>\n      <td>0.340274</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0 -0.547341 -2.082929 -0.190950 -0.327501\n",
       " 1 -1.336805  1.148632 -0.703326 -1.178525\n",
       " 2 -1.114126 -1.051393  2.150010 -1.909319,\n",
       "           0         1         2         3\n",
       " 3 -0.838999 -0.167628 -0.095623 -1.230762\n",
       " 4  0.300337 -0.803076 -1.305823  0.565151\n",
       " 5 -0.160864  2.044033 -0.973718 -1.314739\n",
       " 6  1.039741  0.924924 -0.246127 -1.589007,\n",
       "           0         1         2         3\n",
       " 7 -0.702157 -0.888025 -0.041296  0.184977\n",
       " 8  0.058471 -0.833152 -1.551953  0.679490\n",
       " 9 -0.521986  0.819052 -2.624007  0.340274]"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# 切片\n",
    "pieces = [df[:3],df[3:7],df[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.547341 -2.082929 -0.190950 -0.327501\n",
       "1 -1.336805  1.148632 -0.703326 -1.178525\n",
       "2 -1.114126 -1.051393  2.150010 -1.909319\n",
       "3 -0.838999 -0.167628 -0.095623 -1.230762\n",
       "4  0.300337 -0.803076 -1.305823  0.565151\n",
       "5 -0.160864  2.044033 -0.973718 -1.314739\n",
       "6  1.039741  0.924924 -0.246127 -1.589007\n",
       "7 -0.702157 -0.888025 -0.041296  0.184977\n",
       "8  0.058471 -0.833152 -1.551953  0.679490\n",
       "9 -0.521986  0.819052 -2.624007  0.340274"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.547341</td>\n      <td>-2.082929</td>\n      <td>-0.190950</td>\n      <td>-0.327501</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.336805</td>\n      <td>1.148632</td>\n      <td>-0.703326</td>\n      <td>-1.178525</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.114126</td>\n      <td>-1.051393</td>\n      <td>2.150010</td>\n      <td>-1.909319</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.838999</td>\n      <td>-0.167628</td>\n      <td>-0.095623</td>\n      <td>-1.230762</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.300337</td>\n      <td>-0.803076</td>\n      <td>-1.305823</td>\n      <td>0.565151</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-0.160864</td>\n      <td>2.044033</td>\n      <td>-0.973718</td>\n      <td>-1.314739</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1.039741</td>\n      <td>0.924924</td>\n      <td>-0.246127</td>\n      <td>-1.589007</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-0.702157</td>\n      <td>-0.888025</td>\n      <td>-0.041296</td>\n      <td>0.184977</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.058471</td>\n      <td>-0.833152</td>\n      <td>-1.551953</td>\n      <td>0.679490</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-0.521986</td>\n      <td>0.819052</td>\n      <td>-2.624007</td>\n      <td>0.340274</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "source": [
    "### 6.2 连接  "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key':['foo', 'foo'], 'value':[1,2]})\n",
    "right = pd.DataFrame({'key':['foo','foo'], 'value':[4,5]})\n",
    "# 注意大括号的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   key  value_x  value_y\n",
       "0  foo        1        4\n",
       "1  foo        1        5\n",
       "2  foo        2        4\n",
       "3  foo        2        5"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>key</th>\n      <th>value_x</th>\n      <th>value_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>foo</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>foo</td>\n      <td>1</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>foo</td>\n      <td>2</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>foo</td>\n      <td>2</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "source": [
    "### 6.3 追加"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.682536  1.500781 -0.565660  1.050876\n",
       "1 -0.348679 -0.434257 -0.103393 -0.625979\n",
       "2  0.343289 -0.534163  1.064812 -0.425040\n",
       "3 -0.606479 -0.726873 -0.985852 -0.882931\n",
       "4 -2.016893  0.301592  0.183905  0.075363\n",
       "5  0.574745 -0.352458 -0.118133 -0.971813\n",
       "6 -1.170900  1.239958 -0.904074  1.047417\n",
       "7 -0.788442 -0.777061  1.103504  0.705338"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.682536</td>\n      <td>1.500781</td>\n      <td>-0.565660</td>\n      <td>1.050876</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.348679</td>\n      <td>-0.434257</td>\n      <td>-0.103393</td>\n      <td>-0.625979</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.343289</td>\n      <td>-0.534163</td>\n      <td>1.064812</td>\n      <td>-0.425040</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.606479</td>\n      <td>-0.726873</td>\n      <td>-0.985852</td>\n      <td>-0.882931</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-2.016893</td>\n      <td>0.301592</td>\n      <td>0.183905</td>\n      <td>0.075363</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.574745</td>\n      <td>-0.352458</td>\n      <td>-0.118133</td>\n      <td>-0.971813</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-1.170900</td>\n      <td>1.239958</td>\n      <td>-0.904074</td>\n      <td>1.047417</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-0.788442</td>\n      <td>-0.777061</td>\n      <td>1.103504</td>\n      <td>0.705338</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.682536  1.500781 -0.565660  1.050876\n",
       "1 -0.348679 -0.434257 -0.103393 -0.625979\n",
       "2  0.343289 -0.534163  1.064812 -0.425040\n",
       "3 -0.606479 -0.726873 -0.985852 -0.882931\n",
       "4 -2.016893  0.301592  0.183905  0.075363\n",
       "5  0.574745 -0.352458 -0.118133 -0.971813\n",
       "6 -1.170900  1.239958 -0.904074  1.047417\n",
       "7 -0.788442 -0.777061  1.103504  0.705338"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.682536</td>\n      <td>1.500781</td>\n      <td>-0.565660</td>\n      <td>1.050876</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.348679</td>\n      <td>-0.434257</td>\n      <td>-0.103393</td>\n      <td>-0.625979</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.343289</td>\n      <td>-0.534163</td>\n      <td>1.064812</td>\n      <td>-0.425040</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.606479</td>\n      <td>-0.726873</td>\n      <td>-0.985852</td>\n      <td>-0.882931</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-2.016893</td>\n      <td>0.301592</td>\n      <td>0.183905</td>\n      <td>0.075363</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.574745</td>\n      <td>-0.352458</td>\n      <td>-0.118133</td>\n      <td>-0.971813</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-1.170900</td>\n      <td>1.239958</td>\n      <td>-0.904074</td>\n      <td>1.047417</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-0.788442</td>\n      <td>-0.777061</td>\n      <td>1.103504</td>\n      <td>0.705338</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "source": [
    "## 7. 分组"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.559045 -0.049456\n",
       "1  bar    one  0.681586 -0.550256\n",
       "2  foo    two -1.434285 -0.785119\n",
       "3  bar  three -2.032419 -0.318699\n",
       "4  foo    two -0.330845 -0.448533\n",
       "5  bar    two  0.646538  0.682775\n",
       "6  foo    one -0.012243  1.444787\n",
       "7  foo  three  0.556337  1.417032"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>foo</td>\n      <td>one</td>\n      <td>0.559045</td>\n      <td>-0.049456</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>bar</td>\n      <td>one</td>\n      <td>0.681586</td>\n      <td>-0.550256</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>foo</td>\n      <td>two</td>\n      <td>-1.434285</td>\n      <td>-0.785119</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>bar</td>\n      <td>three</td>\n      <td>-2.032419</td>\n      <td>-0.318699</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>foo</td>\n      <td>two</td>\n      <td>-0.330845</td>\n      <td>-0.448533</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>bar</td>\n      <td>two</td>\n      <td>0.646538</td>\n      <td>0.682775</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>foo</td>\n      <td>one</td>\n      <td>-0.012243</td>\n      <td>1.444787</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>foo</td>\n      <td>three</td>\n      <td>0.556337</td>\n      <td>1.417032</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',\n",
    "   ....:                          'foo', 'bar', 'foo', 'foo'],\n",
    "   ....:                    'B': ['one', 'one', 'two', 'three',\n",
    "   ....:                          'two', 'two', 'one', 'three'],\n",
    "   ....:                    'C': np.random.randn(8),\n",
    "   ....:                    'D': np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -0.704295 -0.186180\n",
       "foo -0.661990  1.578711"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n    <tr>\n      <th>A</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>bar</th>\n      <td>-0.704295</td>\n      <td>-0.186180</td>\n    </tr>\n    <tr>\n      <th>foo</th>\n      <td>-0.661990</td>\n      <td>1.578711</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    0.681586 -0.550256\n",
       "    three -2.032419 -0.318699\n",
       "    two    0.646538  0.682775\n",
       "foo one    0.546802  1.395331\n",
       "    three  0.556337  1.417032\n",
       "    two   -1.765130 -1.233651"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n    <tr>\n      <th>A</th>\n      <th>B</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">bar</th>\n      <th>one</th>\n      <td>0.681586</td>\n      <td>-0.550256</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>-2.032419</td>\n      <td>-0.318699</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>0.646538</td>\n      <td>0.682775</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">foo</th>\n      <th>one</th>\n      <td>0.546802</td>\n      <td>1.395331</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>0.556337</td>\n      <td>1.417032</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>-1.765130</td>\n      <td>-1.233651</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "# 多层索引\n",
    "df.groupby(['A','B']).sum()\n",
    "# 注意中括号"
   ]
  },
  {
   "source": [
    "## 8. 重塑 "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[('bar', 'one'),\n",
       " ('bar', 'two'),\n",
       " ('baz', 'one'),\n",
       " ('baz', 'two'),\n",
       " ('foo', 'one'),\n",
       " ('foo', 'two'),\n",
       " ('qux', 'one'),\n",
       " ('qux', 'two')]"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "   ....:                      'foo', 'foo', 'qux', 'qux'],\n",
    "   ....:                     ['one', 'two', 'one', 'two',\n",
    "   ....:                      'one', 'two', 'one', 'two']]))\n",
    "   ....: \n",
    "tuples\n",
    "# zip用法如下\n",
    "# >>>a = [1,2,3]\n",
    "# >>> b = [4,5,6]\n",
    "# >>> c = [4,5,6,7,8]\n",
    "# >>> zipped = zip(a,b)     # 打包为元组的列表\n",
    "# [(1, 4), (2, 5), (3, 6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "MultiIndex([('bar', 'one'),\n",
       "            ('bar', 'two'),\n",
       "            ('baz', 'one'),\n",
       "            ('baz', 'two'),\n",
       "            ('foo', 'one'),\n",
       "            ('foo', 'two'),\n",
       "            ('qux', 'one'),\n",
       "            ('qux', 'two')],\n",
       "           names=['first', 'second'])"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.266617 -2.198648\n",
       "      two     0.291429 -0.749912\n",
       "baz   one     1.551538  0.306367\n",
       "      two     0.149924 -1.410553\n",
       "foo   one    -1.186484  1.318467\n",
       "      two     1.017068  0.457525\n",
       "qux   one    -0.007335  0.299786\n",
       "      two    -0.079791 -0.240426"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n    </tr>\n    <tr>\n      <th>first</th>\n      <th>second</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">bar</th>\n      <th>one</th>\n      <td>-0.266617</td>\n      <td>-2.198648</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>0.291429</td>\n      <td>-0.749912</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">baz</th>\n      <th>one</th>\n      <td>1.551538</td>\n      <td>0.306367</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>0.149924</td>\n      <td>-1.410553</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">foo</th>\n      <th>one</th>\n      <td>-1.186484</td>\n      <td>1.318467</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>1.017068</td>\n      <td>0.457525</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">qux</th>\n      <th>one</th>\n      <td>-0.007335</td>\n      <td>0.299786</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>-0.079791</td>\n      <td>-0.240426</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.266617\n",
       "               B   -2.198648\n",
       "       two     A    0.291429\n",
       "               B   -0.749912\n",
       "baz    one     A    1.551538\n",
       "               B    0.306367\n",
       "       two     A    0.149924\n",
       "               B   -1.410553\n",
       "foo    one     A   -1.186484\n",
       "               B    1.318467\n",
       "       two     A    1.017068\n",
       "               B    0.457525\n",
       "qux    one     A   -0.007335\n",
       "               B    0.299786\n",
       "       two     A   -0.079791\n",
       "               B   -0.240426\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "# stack压缩\n",
    "stacked = df.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.266617 -2.198648\n",
       "      two     0.291429 -0.749912\n",
       "baz   one     1.551538  0.306367\n",
       "      two     0.149924 -1.410553\n",
       "foo   one    -1.186484  1.318467\n",
       "      two     1.017068  0.457525\n",
       "qux   one    -0.007335  0.299786\n",
       "      two    -0.079791 -0.240426"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n    </tr>\n    <tr>\n      <th>first</th>\n      <th>second</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">bar</th>\n      <th>one</th>\n      <td>-0.266617</td>\n      <td>-2.198648</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>0.291429</td>\n      <td>-0.749912</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">baz</th>\n      <th>one</th>\n      <td>1.551538</td>\n      <td>0.306367</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>0.149924</td>\n      <td>-1.410553</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">foo</th>\n      <th>one</th>\n      <td>-1.186484</td>\n      <td>1.318467</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>1.017068</td>\n      <td>0.457525</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">qux</th>\n      <th>one</th>\n      <td>-0.007335</td>\n      <td>0.299786</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>-0.079791</td>\n      <td>-0.240426</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "# 压缩后具有多层索引\n",
    "# 默认还原最后一列\n",
    "stacked.unstack()"
   ]
  },
  {
   "source": [
    "## 9. 数据透视表"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo  1.689671 -0.698901\n",
       "1     one  B  foo -0.990147  1.670040\n",
       "2     two  C  foo  1.779018 -1.289200\n",
       "3   three  A  bar -1.012070  1.257548\n",
       "4     one  B  bar  2.928475  0.029098\n",
       "5     one  C  bar  1.272499 -0.592279\n",
       "6     two  A  foo  1.209681 -1.252153\n",
       "7   three  B  foo -0.587989  1.174857\n",
       "8     one  C  foo -1.235629  1.915401\n",
       "9     one  A  bar -1.043050  0.928881\n",
       "10    two  B  bar  1.711671 -1.200868\n",
       "11  three  C  bar  0.467063  0.715259"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>one</td>\n      <td>A</td>\n      <td>foo</td>\n      <td>1.689671</td>\n      <td>-0.698901</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>one</td>\n      <td>B</td>\n      <td>foo</td>\n      <td>-0.990147</td>\n      <td>1.670040</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>two</td>\n      <td>C</td>\n      <td>foo</td>\n      <td>1.779018</td>\n      <td>-1.289200</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>three</td>\n      <td>A</td>\n      <td>bar</td>\n      <td>-1.012070</td>\n      <td>1.257548</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>one</td>\n      <td>B</td>\n      <td>bar</td>\n      <td>2.928475</td>\n      <td>0.029098</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>one</td>\n      <td>C</td>\n      <td>bar</td>\n      <td>1.272499</td>\n      <td>-0.592279</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>two</td>\n      <td>A</td>\n      <td>foo</td>\n      <td>1.209681</td>\n      <td>-1.252153</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>three</td>\n      <td>B</td>\n      <td>foo</td>\n      <td>-0.587989</td>\n      <td>1.174857</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>one</td>\n      <td>C</td>\n      <td>foo</td>\n      <td>-1.235629</td>\n      <td>1.915401</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>one</td>\n      <td>A</td>\n      <td>bar</td>\n      <td>-1.043050</td>\n      <td>0.928881</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>two</td>\n      <td>B</td>\n      <td>bar</td>\n      <td>1.711671</td>\n      <td>-1.200868</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>three</td>\n      <td>C</td>\n      <td>bar</td>\n      <td>0.467063</td>\n      <td>0.715259</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,\n",
    "   .....:                    'B': ['A', 'B', 'C'] * 4,\n",
    "   .....:                    'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "   .....:                    'D': np.random.randn(12),\n",
    "   .....:                    'E': np.random.randn(12)})\n",
    "   .....:\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A -1.043050  1.689671\n",
       "      B  2.928475 -0.990147\n",
       "      C  1.272499 -1.235629\n",
       "three A -1.012070       NaN\n",
       "      B       NaN -0.587989\n",
       "      C  0.467063       NaN\n",
       "two   A       NaN  1.209681\n",
       "      B  1.711671       NaN\n",
       "      C       NaN  1.779018"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>C</th>\n      <th>bar</th>\n      <th>foo</th>\n    </tr>\n    <tr>\n      <th>A</th>\n      <th>B</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">one</th>\n      <th>A</th>\n      <td>-1.043050</td>\n      <td>1.689671</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>2.928475</td>\n      <td>-0.990147</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>1.272499</td>\n      <td>-1.235629</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">three</th>\n      <th>A</th>\n      <td>-1.012070</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>NaN</td>\n      <td>-0.587989</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>0.467063</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">two</th>\n      <th>A</th>\n      <td>NaN</td>\n      <td>1.209681</td>\n    </tr>\n    <tr>\n      <th>B</th>\n      <td>1.711671</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>C</th>\n      <td>NaN</td>\n      <td>1.779018</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "source": [
    "## 10. 可视化"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21f941fdfd0>"
      ]
     },
     "metadata": {},
     "execution_count": 53
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {}
    },
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     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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125.642653 \r\nL 120.764147 126.208759 \r\nL 121.099282 127.32114 \r\nL 121.434417 127.86333 \r\nL 121.769552 126.583219 \r\nL 122.104687 125.988941 \r\nL 122.439823 127.833592 \r\nL 122.774958 127.085976 \r\nL 123.110093 123.355133 \r\nL 123.445228 121.873631 \r\nL 123.780363 122.471565 \r\nL 124.115498 120.983445 \r\nL 124.450633 116.632653 \r\nL 124.785769 117.67427 \r\nL 125.120904 115.01881 \r\nL 125.456039 118.8535 \r\nL 125.791174 118.409566 \r\nL 126.126309 120.767111 \r\nL 126.461444 114.726418 \r\nL 126.796579 112.822727 \r\nL 127.131715 113.427054 \r\nL 127.46685 110.779811 \r\nL 127.801985 110.937491 \r\nL 128.13712 111.37304 \r\nL 128.472255 113.712632 \r\nL 128.80739 113.789823 \r\nL 129.142525 113.646057 \r\nL 129.47766 111.912454 \r\nL 129.812796 113.062029 \r\nL 130.147931 115.698 \r\nL 130.483066 115.286984 \r\nL 130.818201 115.15964 \r\nL 131.153336 114.325578 \r\nL 131.488471 116.947546 \r\nL 131.823606 117.347684 \r\nL 132.158742 117.42701 \r\nL 132.493877 115.85489 \r\nL 133.164147 111.4856 \r\nL 133.834417 113.633416 \r\nL 134.504687 112.028895 \r\nL 134.839823 110.622427 \r\nL 135.174958 112.442281 \r\nL 135.510093 109.884823 \r\nL 135.845228 109.354717 \r\nL 136.180363 108.318289 \r\nL 136.515498 110.775246 \r\nL 136.850633 110.26143 \r\nL 137.185769 106.189964 \r\nL 137.520904 105.82139 \r\nL 137.856039 110.446268 \r\nL 138.191174 107.394292 \r\nL 138.526309 107.983139 \r\nL 138.861444 103.464169 \r\nL 139.531715 104.998114 \r\nL 139.86685 105.443077 \r\nL 140.201985 105.05594 \r\nL 140.53712 107.642407 \r\nL 140.872255 109.058157 \r\nL 141.20739 109.559497 \r\nL 141.542525 111.418765 \r\nL 141.87766 108.940388 \r\nL 142.212796 110.356617 \r\nL 142.883066 111.485035 \r\nL 143.218201 113.048788 \r\nL 143.553336 111.962772 \r\nL 143.888471 112.373111 \r\nL 144.223606 111.04174 \r\nL 144.558742 108.859571 \r\nL 144.893877 112.103811 \r\nL 145.229012 111.042429 \r\nL 145.564147 110.788881 \r\nL 145.899282 107.779493 \r\nL 146.234417 111.37907 \r\nL 146.569552 109.789132 \r\nL 146.904687 110.679301 \r\nL 147.239823 109.793338 \r\nL 147.574958 106.610147 \r\nL 147.910093 105.381629 \r\nL 148.245228 105.604568 \r\nL 148.580363 103.359505 \r\nL 148.915498 102.612514 \r\nL 149.250633 101.337295 \r\nL 149.585769 104.254443 \r\nL 149.920904 99.748287 \r\nL 150.256039 97.415844 \r\nL 150.591174 97.626823 \r\nL 150.926309 97.306368 \r\nL 151.261444 91.735158 \r\nL 151.931715 94.191563 \r\nL 152.26685 94.431327 \r\nL 152.601985 100.689689 \r\nL 152.93712 99.746119 \r\nL 153.272255 101.652437 \r\nL 153.60739 99.874189 \r\nL 154.27766 102.161839 \r\nL 154.612796 105.062773 \r\nL 154.947931 103.752195 \r\nL 155.283066 104.451979 \r\nL 155.618201 103.782314 \r\nL 156.288471 98.27322 \r\nL 156.958742 101.52034 \r\nL 157.293877 102.01251 \r\nL 157.629012 97.655411 \r\nL 157.964147 98.018771 \r\nL 158.299282 98.646418 \r\nL 158.634417 101.08737 \r\nL 158.969552 100.354419 \r\nL 159.304688 101.063715 \r\nL 159.974958 94.353752 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69.309732 \r\nL 187.791174 69.668623 \r\nL 188.126309 68.595814 \r\nL 188.461444 69.623482 \r\nL 188.796579 68.553132 \r\nL 189.131715 65.267139 \r\nL 189.46685 66.285465 \r\nL 189.801985 66.545406 \r\nL 190.13712 68.401314 \r\nL 190.80739 74.019117 \r\nL 191.142525 73.583323 \r\nL 191.47766 70.971125 \r\nL 191.812796 70.349188 \r\nL 192.147931 71.65959 \r\nL 192.483066 70.860752 \r\nL 192.818201 69.210254 \r\nL 193.153336 70.470777 \r\nL 193.488471 70.648206 \r\nL 194.158742 72.154855 \r\nL 194.493877 73.654143 \r\nL 194.829012 71.825023 \r\nL 195.164147 72.094346 \r\nL 195.499282 71.633566 \r\nL 195.834417 69.941051 \r\nL 196.169552 70.283463 \r\nL 196.504687 76.445327 \r\nL 196.839823 76.07556 \r\nL 197.174958 79.706696 \r\nL 197.510093 78.249663 \r\nL 197.845228 79.370858 \r\nL 198.180363 77.187376 \r\nL 198.515498 77.134729 \r\nL 198.850633 78.983316 \r\nL 199.185769 81.73992 \r\nL 199.520904 80.877472 \r\nL 199.856039 83.285189 \r\nL 200.191174 87.61187 \r\nL 200.526309 86.481046 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92.12518 \r\nL 216.947931 96.34275 \r\nL 217.283066 93.017807 \r\nL 217.618201 94.106616 \r\nL 217.953336 92.731359 \r\nL 218.288471 90.532411 \r\nL 218.623606 93.145244 \r\nL 218.958742 92.969102 \r\nL 219.293877 93.307888 \r\nL 219.629012 91.920895 \r\nL 219.964147 93.176708 \r\nL 220.299282 91.692095 \r\nL 220.969552 90.241332 \r\nL 221.304688 91.588134 \r\nL 221.639823 88.308036 \r\nL 221.974958 86.529604 \r\nL 222.310093 87.202661 \r\nL 222.645228 90.065171 \r\nL 222.980363 86.778988 \r\nL 223.315498 87.206222 \r\nL 223.650633 86.801927 \r\nL 223.985769 87.062801 \r\nL 224.320904 83.345451 \r\nL 224.656039 82.423082 \r\nL 224.991174 75.818439 \r\nL 225.326309 75.059943 \r\nL 225.661444 72.435826 \r\nL 225.996579 73.606778 \r\nL 226.66685 73.094574 \r\nL 227.001985 72.65679 \r\nL 227.33712 75.311391 \r\nL 227.672255 76.462513 \r\nL 228.00739 78.4694 \r\nL 228.342525 77.663018 \r\nL 228.67766 79.500461 \r\nL 229.012796 75.520401 \r\nL 229.347931 74.17548 \r\nL 229.683066 76.205731 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243.758742 71.418921 \r\nL 244.093877 72.629907 \r\nL 244.764147 70.728442 \r\nL 245.099282 67.412317 \r\nL 245.434417 70.612542 \r\nL 245.769552 72.590342 \r\nL 246.104687 72.328178 \r\nL 246.439823 70.482244 \r\nL 246.774958 71.031354 \r\nL 247.110093 75.143654 \r\nL 247.445228 76.869624 \r\nL 247.780363 75.404842 \r\nL 248.115498 74.937116 \r\nL 248.785769 70.431971 \r\nL 249.120904 68.950152 \r\nL 249.456039 70.857644 \r\nL 249.791174 75.814875 \r\nL 250.126309 72.737818 \r\nL 250.461444 73.035856 \r\nL 251.131715 68.965762 \r\nL 251.46685 70.791746 \r\nL 251.801985 73.423692 \r\nL 252.13712 74.350554 \r\nL 252.472255 78.197689 \r\nL 252.80739 75.526982 \r\nL 253.47766 78.13461 \r\nL 253.812796 75.264613 \r\nL 254.147931 74.28765 \r\nL 254.483066 74.939112 \r\nL 254.818201 73.744732 \r\nL 255.153336 74.491479 \r\nL 255.488471 75.793234 \r\nL 255.823606 76.558896 \r\nL 256.158742 77.909105 \r\nL 256.493877 76.962058 \r\nL 256.829012 77.586126 \r\nL 257.164147 77.338302 \r\nL 257.499282 73.177753 \r\nL 257.834417 70.920042 \r\nL 258.169552 75.092009 \r\nL 258.839823 76.650056 \r\nL 259.174958 72.975977 \r\nL 259.510093 71.783703 \r\nL 259.845228 73.731836 \r\nL 260.180363 74.492399 \r\nL 260.515498 73.669582 \r\nL 261.185769 75.023911 \r\nL 261.520904 72.02277 \r\nL 261.856039 71.905406 \r\nL 262.191174 70.862073 \r\nL 262.526309 73.64838 \r\nL 262.861444 73.859512 \r\nL 263.196579 72.478241 \r\nL 263.531715 69.837869 \r\nL 263.86685 70.966006 \r\nL 264.201985 67.076194 \r\nL 264.53712 68.663699 \r\nL 264.872255 67.280646 \r\nL 265.20739 66.790762 \r\nL 265.542525 68.52343 \r\nL 265.87766 67.472657 \r\nL 266.212796 68.597865 \r\nL 266.547931 66.71892 \r\nL 266.883066 66.66651 \r\nL 267.218201 65.256707 \r\nL 267.553336 65.082358 \r\nL 267.888471 63.1773 \r\nL 268.223606 63.887713 \r\nL 268.558742 63.392524 \r\nL 268.893877 64.206642 \r\nL 269.229012 66.510731 \r\nL 269.564147 67.985585 \r\nL 269.899282 67.674862 \r\nL 270.234417 69.501697 \r\nL 270.569552 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176.396579 66.639517 \r\nL 177.06685 65.604533 \r\nL 177.401985 65.998287 \r\nL 177.73712 64.884784 \r\nL 178.072255 66.647416 \r\nL 178.40739 64.917614 \r\nL 178.742525 65.447351 \r\nL 179.07766 65.298504 \r\nL 179.412796 65.766875 \r\nL 179.747931 66.477914 \r\nL 180.418201 62.21014 \r\nL 180.753336 63.033394 \r\nL 181.423606 56.336525 \r\nL 181.758742 53.789443 \r\nL 182.093877 54.075213 \r\nL 182.429012 57.584808 \r\nL 182.764147 59.509307 \r\nL 183.099282 58.160531 \r\nL 183.434417 62.061998 \r\nL 183.769552 60.446259 \r\nL 184.104687 63.097641 \r\nL 184.439823 64.628297 \r\nL 184.774958 62.367686 \r\nL 185.445228 56.619285 \r\nL 185.780363 56.566908 \r\nL 186.115498 56.656549 \r\nL 186.450633 59.156442 \r\nL 186.785769 60.445502 \r\nL 187.120904 62.997342 \r\nL 187.456039 62.24494 \r\nL 187.791174 62.546233 \r\nL 188.126309 59.664858 \r\nL 188.461444 63.190252 \r\nL 188.796579 64.616406 \r\nL 189.131715 63.61665 \r\nL 189.46685 64.411948 \r\nL 189.801985 65.791769 \r\nL 190.13712 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178.40739 130.518312 \r\nL 179.07766 125.300476 \r\nL 179.412796 120.754268 \r\nL 179.747931 120.541779 \r\nL 180.083066 118.481046 \r\nL 180.418201 120.852261 \r\nL 180.753336 120.296546 \r\nL 181.088471 121.313773 \r\nL 181.423606 125.9088 \r\nL 182.093877 127.023992 \r\nL 182.429012 130.624346 \r\nL 182.764147 131.189073 \r\nL 183.099282 132.265878 \r\nL 183.434417 132.908265 \r\nL 183.769552 132.879581 \r\nL 184.104687 133.781775 \r\nL 184.439823 132.475066 \r\nL 185.110093 140.126142 \r\nL 185.445228 141.286122 \r\nL 185.780363 141.914687 \r\nL 186.115498 143.164656 \r\nL 187.120904 136.76419 \r\nL 187.456039 138.985273 \r\nL 187.791174 138.769214 \r\nL 188.126309 137.723721 \r\nL 188.461444 140.836723 \r\nL 188.796579 140.453432 \r\nL 189.801985 147.663515 \r\nL 190.13712 147.869486 \r\nL 190.472255 151.087466 \r\nL 190.80739 147.213351 \r\nL 191.142525 149.751496 \r\nL 191.47766 151.08461 \r\nL 191.812796 150.514182 \r\nL 192.147931 151.146621 \r\nL 192.483066 151.287038 \r\nL 192.818201 150.96208 \r\nL 193.153336 151.185737 \r\nL 193.488471 149.731356 \r\nL 193.823606 152.292039 \r\nL 194.158742 150.549141 \r\nL 194.493877 151.614078 \r\nL 194.829012 150.598986 \r\nL 195.164147 151.681324 \r\nL 195.499282 151.202615 \r\nL 196.169552 147.385524 \r\nL 196.839823 147.363481 \r\nL 197.174958 146.714049 \r\nL 197.845228 150.488993 \r\nL 198.180363 150.998639 \r\nL 198.515498 149.407539 \r\nL 198.850633 152.691279 \r\nL 199.185769 154.668284 \r\nL 199.520904 157.670101 \r\nL 199.856039 157.628574 \r\nL 200.191174 159.72414 \r\nL 200.861444 164.65567 \r\nL 201.531715 162.77995 \r\nL 201.86685 164.019921 \r\nL 202.201985 162.452815 \r\nL 202.53712 164.354658 \r\nL 202.872255 164.830314 \r\nL 203.20739 167.13495 \r\nL 203.542525 168.43762 \r\nL 204.547931 174.890376 \r\nL 204.883066 169.284923 \r\nL 205.218201 170.794968 \r\nL 205.553336 171.787587 \r\nL 205.888471 173.189661 \r\nL 206.223606 173.599333 \r\nL 206.558742 171.499666 \r\nL 206.893877 171.012062 \r\nL 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199.757388 \r\nL 328.883066 199.684515 \r\nL 329.218201 200.496186 \r\nL 329.888471 196.403847 \r\nL 330.223606 200.01516 \r\nL 330.558742 199.828945 \r\nL 330.893877 198.831337 \r\nL 331.229012 196.855471 \r\nL 331.564147 197.423704 \r\nL 331.899282 201.485317 \r\nL 332.234417 200.959278 \r\nL 332.569552 198.582082 \r\nL 332.904687 197.994083 \r\nL 333.239823 195.315286 \r\nL 333.574958 197.831087 \r\nL 333.910093 196.27807 \r\nL 334.245228 196.090583 \r\nL 334.580363 195.45568 \r\nL 334.915498 195.397193 \r\nL 335.250633 193.627225 \r\nL 335.585769 192.783612 \r\nL 335.920904 189.554994 \r\nL 336.256039 191.122443 \r\nL 336.591174 194.18026 \r\nL 336.926309 196.134938 \r\nL 337.261444 199.319422 \r\nL 337.596579 200.429974 \r\nL 337.931715 200.039977 \r\nL 338.26685 199.834841 \r\nL 338.601985 197.743985 \r\nL 338.93712 197.163254 \r\nL 339.272255 197.241313 \r\nL 339.942525 199.790226 \r\nL 340.27766 199.472361 \r\nL 340.612796 201.524038 \r\nL 340.947931 196.362055 \r\nL 341.283066 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "ts = pd.DataFrame(np.random.randn(1000, 4),\n",
    "   .....:                index=pd.date_range('1/1/2000', periods=1000), columns=['A', 'B', 'C', 'D'])\n",
    "   .....: \n",
    "ts = ts.cumsum() \n",
    "# 计算累计和\n",
    "plt.figure()\n",
    "ts.plot()\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "source": [
    "## 11. 数据读入/读出"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset\\housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20433.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.569704</td>\n      <td>35.631861</td>\n      <td>28.639486</td>\n      <td>2635.763081</td>\n      <td>537.870553</td>\n      <td>1425.476744</td>\n      <td>499.539680</td>\n      <td>3.870671</td>\n      <td>206855.816909</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.003532</td>\n      <td>2.135952</td>\n      <td>12.585558</td>\n      <td>2181.615252</td>\n      <td>421.385070</td>\n      <td>1132.462122</td>\n      <td>382.329753</td>\n      <td>1.899822</td>\n      <td>115395.615874</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.350000</td>\n      <td>32.540000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.499900</td>\n      <td>14999.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.800000</td>\n      <td>33.930000</td>\n      <td>18.000000</td>\n      <td>1447.750000</td>\n      <td>296.000000</td>\n      <td>787.000000</td>\n      <td>280.000000</td>\n      <td>2.563400</td>\n      <td>119600.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.490000</td>\n      <td>34.260000</td>\n      <td>29.000000</td>\n      <td>2127.000000</td>\n      <td>435.000000</td>\n      <td>1166.000000</td>\n      <td>409.000000</td>\n      <td>3.534800</td>\n      <td>179700.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-118.010000</td>\n      <td>37.710000</td>\n      <td>37.000000</td>\n      <td>3148.000000</td>\n      <td>647.000000</td>\n      <td>1725.000000</td>\n      <td>605.000000</td>\n      <td>4.743250</td>\n      <td>264725.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.310000</td>\n      <td>41.950000</td>\n      <td>52.000000</td>\n      <td>39320.000000</td>\n      <td>6445.000000</td>\n      <td>35682.000000</td>\n      <td>6082.000000</td>\n      <td>15.000100</td>\n      <td>500001.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   longitude           20640 non-null  float64\n 1   latitude            20640 non-null  float64\n 2   housing_median_age  20640 non-null  float64\n 3   total_rooms         20640 non-null  float64\n 4   total_bedrooms      20433 non-null  float64\n 5   population          20640 non-null  float64\n 6   households          20640 non-null  float64\n 7   median_income       20640 non-null  float64\n 8   median_house_value  20640 non-null  float64\n 9   ocean_proximity     20640 non-null  object \ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "5    -122.25     37.85                52.0        919.0           213.0   \n",
       "6    -122.25     37.84                52.0       2535.0           489.0   \n",
       "7    -122.25     37.84                52.0       3104.0           687.0   \n",
       "8    -122.26     37.84                42.0       2555.0           665.0   \n",
       "9    -122.25     37.84                52.0       3549.0           707.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
       "5       413.0       193.0         4.0368            269700.0        NEAR BAY  \n",
       "6      1094.0       514.0         3.6591            299200.0        NEAR BAY  \n",
       "7      1157.0       647.0         3.1200            241400.0        NEAR BAY  \n",
       "8      1206.0       595.0         2.0804            226700.0        NEAR BAY  \n",
       "9      1551.0       714.0         3.6912            261100.0        NEAR BAY  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1627.0</td>\n      <td>280.0</td>\n      <td>565.0</td>\n      <td>259.0</td>\n      <td>3.8462</td>\n      <td>342200.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>919.0</td>\n      <td>213.0</td>\n      <td>413.0</td>\n      <td>193.0</td>\n      <td>4.0368</td>\n      <td>269700.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-122.25</td>\n      <td>37.84</td>\n      <td>52.0</td>\n      <td>2535.0</td>\n      <td>489.0</td>\n      <td>1094.0</td>\n      <td>514.0</td>\n      <td>3.6591</td>\n      <td>299200.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-122.25</td>\n      <td>37.84</td>\n      <td>52.0</td>\n      <td>3104.0</td>\n      <td>687.0</td>\n      <td>1157.0</td>\n      <td>647.0</td>\n      <td>3.1200</td>\n      <td>241400.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-122.26</td>\n      <td>37.84</td>\n      <td>42.0</td>\n      <td>2555.0</td>\n      <td>665.0</td>\n      <td>1206.0</td>\n      <td>595.0</td>\n      <td>2.0804</td>\n      <td>226700.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-122.25</td>\n      <td>37.84</td>\n      <td>52.0</td>\n      <td>3549.0</td>\n      <td>707.0</td>\n      <td>1551.0</td>\n      <td>714.0</td>\n      <td>3.6912</td>\n      <td>261100.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}